山东CORS基准站坐标时间序列噪声分析

针对目前在分析GPS基准站噪声时对其坐标时间序列周期项提取不充分的现状,该文提出用小波谱探测坐标时间序列中的周期项,用小波分析方法提取周期项,得到不含明显周期项噪声序列。以山东CORS中26个基准站两年的坐标时间序列为例,去除了坐标时间序列中阶跃项,确定了长期趋势项,对周期项进行了探测、分离和提取,得到了各测站的噪声序列;用谱指数法分析了噪声的类型,用最大似然估计获取了各种噪声的分量,进而建立了噪声组合模型。实验结果表明,山东CORS基准站噪声模型可以用白噪声加闪烁噪声的形式来描述,其白噪声的平均值为1.25mm,闪烁噪声的平均值为5.46mm。     

基于六参数仿射变换的控制点选取及坐标校正分析

以经纬度坐标转换到深圳坐标为例,利用卫星遥感影像,通过全局搜寻算法选择控制点对求解仿射变换六参数值,在不同约束目标下对坐标距离偏差程度进行筛选分析,确定适合深圳坐标下能有效提高校正精度的控制点对组合及校正坐标公式。     

A global,high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km² of inland water globally – nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal ‘inland water’ biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high, with commission errors <4% and omission errors <14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds – effects which were compounded by differences in image acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset is available for open access at the GLCF website (http://www.landcover.org).     

利用最新空间数据检测固体地球形态变化研究

固体地球形态变化作为一个古老的科学话题一直受到地球动力学研究学者的关注和讨论,本文搜集了全球近4 000个空间技术台站最新实测数据,利用经典的Delaunay三角网构建地球数学模型,对固体地球表面积和体积变化进行重新计算,实验发现:固体地球整体存在压缩趋势,而且全球均存在不同程度的非对称构造变化。     

融合谱间特征的高分辨率遥感影像分类

传统影像分类方法仅利用灰度、纹理等谱内特征,未能充分利用谱间特征,针对这一不足,本文提出一种融合谱间特征的高分辨率遥感影像分类方法。采用主成分分析法(Principal Component Analysis,PCA)对遥感影像进行变换,提取前两个主分量作为变换后的数据;通过影像分割方法获取像斑,选取训练样本像斑;利用灰度直方图与联合灰度直方图分别表达像斑的谱内特征与谱间特征,采用G统计量度量直方图距离,依据距离倒数加权计算像斑的谱内概率与谱间概率,依据加权组合谱内概率与谱间概率构建联合概率,在联合概率最大基础上获取影像分类结果。在Quick Bird遥感影像上的实验结果表明了本文方法的有效性,总体分类精度与kappa系数分别达到了90.0%和86.7%。     

基于Markovian切换的时滞回归神经网络Lagrange全局均方指数稳定

对一类激励函数是Lurie型（包括有界和无界激励函数）的具有Markovian切换的时滞回归神经网络的Lagrange全局均方指数稳定性进行了研究,得到了回归神经网络在Markovian切换状态下的Lagrange全局均方指数稳定的充分判据,并通过数值例子验证了所得结论的正确性和有效性.     

Error assessment of grid-based diffuse solar radiation models

Two grid-based diffuse solar radiation models, ESRI’s Solar Analyst (SA) and Kumar’s model (KM), were assessed using a data-independent approach where each model’s numerical results of clear sky diffuse radiation on V/U-shaped surfaces were compared with analytical results derived using each model’s assumptions. SA and KM consistently underestimate and overestimate, respectively, diffuse radiation at daily, seasonal, and annual scale relative to the analytical results based on each model’s parameterizations. Overall, SA performs better than KM in modeling diffuse radiation at most timescales. While SA and KM have similar error in calculating diffuse radiation on a horizontal surface, SA models sky view factor much better than KM, with mean absolute relative differences of 0.76% and 17.02%, respectively. KM has a large error in sky view factor as it does not consider the shading effect from surrounding terrain. Sky view factor error in SA is small and use of more zenith divisions can further reduce the error. Based on our previous study, model performance on clear sky global solar radiation was also evaluated. Overall, KM performs better than SA in global radiation as KM performs better than SA in modeling direct radiation which is the major component of global radiation.     

六边形格网的快速傅里叶变换

全球离散格网系统是支持多源地球空间数据融合处理的新型数据模型,傅里叶变换是联系其空域与频域的纽带,对以格网系统为框架的遥感图像的增强具有重要意义。根据三孔六边形格网剖分方案,设计了一种规则六边形格网单元组合结构,并将该结构空间域的二维离散傅里叶变换转化为可以用蝶形算法加速的标准一维变换。采用遥感影像进行实验验证,结果表明本文提出的快速傅里叶变换算法正确、高效,其复杂度为N log2N。随着数据量增大,效率以指数函数形式提高。     

利用卷积神经网络的高光谱图像分类

针对高光谱图像分类中对光谱信息利用不足的问题,提出一种基于卷积神经网络在光谱域开展的分类算法。该算法通过构建五层网络结构,逐像素对光谱信息开展分析,将全光谱段集合作为输入,利用神经网络展开代价函数值的计算,实现对光谱特征的提取与分类。实验中采用三组高光谱遥感影像数据进行对比分析,以India Pines数据集为例,提出的基于卷积神经网络的分类方法的分类正确率达到90.16%,比RBF-SVM方法高出2.56%,相比三种传统的深度学习方法高出1%~3%,训练速度也较为理想。实验结果表明,本文所提出的算法充分利用了高光谱图像中逐像素点的光谱域信息,能够有效提高分类正确率。与传统学习算法相比,在较少训练样本的情况下,更能发挥其良好的分类性能。     

华北地区极端降水变化特征及多模式模拟评估

利用1961—2010年华北地区68个台站逐日降水数据计算了6个极端降水指数,对华北极端降水的时空变化特征进行了分析。进一步评估了CMIP5的31个全球气候模式对华北地区极端降水的模拟效果。结果表明:1华北地区极端降水指数具有较大地区性差异,平均日降水强度、极端降水阈值等指标东南高西北低,大雨日数和连续干日东西分布差异明显。6个极端降水指标的年际变化显示华北地区强降水事件增多的同时,极端干旱程度加剧。2CMIP5模式对华北地区极端降水指数的模拟差异较大,其中对平均日降水强度和极端降水阈值空间分布模拟能力较好,有1/3模式的空间相关系数超过了0.6。同一模式对不同指数的模拟能力也存在较大的差异。3模式模拟相关系数和均方根误差有较好的一致性,根据这两个指标分别得到两组模式排序,其相关系数超过了0.4。HadGEM2-CC,HadGEM2-ES,MIROC4h,MIROC5以及CCSM4是模拟华北地区极端指数最好的5个全球模式。相比于多模式集合平均,优选模式集合能够显著降低模拟的湿偏差。